Vision is usually treated as one capability, and in the current practice it is bought whole: an image is passed to a network trained on millions of labelled pictures, which returns names. We take a different view. Seeing decomposes into two quite different acts — perceiving structure (there is a large rounded region here, moving, brighter than its surround) and recognising a concept (that region is a face). The first act has been solved by algorithms for two decades, with no learning at all [2][3][4]: segmentation, region and object proposals, saliency, local-feature description and instance matching, optical flow. The second act — naming a novel category — is the only place a learned model is genuinely required, and, tellingly, every recent “training-free” recogniser supplies it not by any new algorithm but by leaning on a pretrained perceptual backbone [11][12][13][14]. Our thesis is that a cognitive substrate which already learns concepts by induction — from a handful of examples, over a knowledge graph it reasons on, holding graded beliefs and abstaining when it cannot ground an answer — can supply the second half of seeing itself, occupying the role the backbone plays, with no neural network anywhere in the loop. We describe the architecture and, as its centre, a methodological commitment: vision is built into the substrate as a faculty — one entry point for all sight, the way the substrate already has one reader that turns a sentence into propositions and one voice that renders its state into words. The faculty perceives structure with classical vision, admits what it sees through the substrate's single knowledge-admission door as typed, provenance-bearing observations, and lets the substrate learn what those structures mean exactly as it learns everything else. Structure is perceived; meaning is learned; the two are kept separate, and that separation is what makes machine sight legible.
Ask what it takes to see a person walk through a doorway and you are really asking two questions with different answers. The first is geometric and photometric: where in the frame is there a coherent thing, how big is it, what is its shape, what are its colours, is it moving and how. The second is semantic: what is that thing — a person, a door, a shadow. David Marr made the case forty years ago that vision is layered in exactly this way, that a system recovers structure before it assigns meaning, and that conflating the layers is a mistake of method, not just of engineering [1].
The modern practice conflates them on purpose. A convolutional network [10] or a vision transformer is handed raw pixels and returns names, having folded perception and recognition into one trained mapping. This works, and it hides the seam. But the seam is real, and where it lies decides what actually needs a model. Almost everything on the structure side of the seam can be computed directly from an image, deterministically, with no training. Only the meaning side — putting a name to a category the system has never been told about — genuinely requires a learner. The interesting question, for a system that is not built on a trained perceptual backbone, is whether it can be the learner for that second half itself.
Vision is not one capability to be acquired whole. It is the composition of a perception that algorithms already perform and a recognition that must be learned — and the recognition can be learned by the substrate that will reason over it, rather than bought from a network trained elsewhere.
The structure side of vision is not a frontier; it is a mature body of work, and it is worth being concrete about how much of “seeing” it delivers with no learning in the loop.
The one classical recogniser that crosses into semantics — the boosted cascade that detects faces [16] — does so precisely by training a classifier on labelled examples, which is the exception that proves where the seam lies. Everything above it in this list needs no examples; the moment a novel category must be named, learning enters.
So there is a wall, and it is worth stating precisely rather than vaguely. Classical vision will tell you there is a coherent, salient, roughly round region in the upper-centre of the frame, skin-toned, moving leftward, textured like the reference image of a particular person. What it will not do, unprompted, is emit the word face for a category it has never been shown. Assigning an open-ended name to a novel visual category is the part that requires a learner.
The instructive fact is how the recent literature clears this wall. A wave of “training-free” open-vocabulary recognisers has appeared, and the phrase is easy to misread. They are training-free only in the sense that they add no new fine-tuning. The semantics they emit comes entirely from a pretrained perceptual backbone: methods that assign names to regions do so by harnessing vision foundation models — CLIP for image–text alignment [11], DINO for emergent object structure [12], SAM for class-agnostic masks [13] — stitched together at inference [14]. Strip the backbones out and the meaning goes with them; what remains is exactly the structure-level machinery of §2. The honest reading of the state of the art is that there is no algorithm that assigns open-category names without a model that learned those names from data somewhere. The meaning is always learned. The only question is by what, and where.
Every current recogniser fills the semantic half of vision with a network trained on labelled images. The substrate can fill it instead — with the same faculty it already uses to learn concepts from a few examples — and thereby own the whole of sight without a perceptual model in the loop.
The reason this is not wishful is that the components the semantic half of vision needs are not new capabilities to be invented; they are faculties the substrate already runs, for language and for action. Naming a perceived structure is a learning-and-reasoning problem, and the substrate is a learning-and-reasoning system.
These are the exact ingredients a recogniser needs: a learner to map signatures to categories, a place to put the result so it participates in inference, a way to hold it as revisable belief, and a record of where it came from. The substrate has them already. What it lacks is a way for pixels to reach them.
The way pixels should reach the substrate is not as one more caller that writes observations, but as a faculty — a first-class organ of the substrate with a single entry point, the sole route by which visual input becomes knowledge. This is the substrate’s governing pattern, applied to sight. The substrate does not have many ad-hoc ways to read text; it has one reader, which turns a sentence into propositions, and every path that consumes language goes through it. It does not have scattered ways to speak; it has one voice, which renders its internal state into words. Vision is built the same way: one faculty, the single entry point for all sight, so that “what the substrate has seen” has one authoritative answer and one place it is produced — never a second, private path that admits pixels behind the faculty’s back.
The parallel to the reader is exact and worth drawing out, because it is the whole method. The reader does not understand a sentence by an inscrutable mapping; it derives a proposition from a token sequence by a procedure the substrate can exhibit, and the meanings of the words are learned, held in the same store as everything else. The vision faculty does not understand an image by an inscrutable mapping; it derives observations from a pixel array by algorithm, and the meanings of the structures are learned, held in the same store. Same shape, different modality. Sight is to pixels what reading is to text.
The faculty has three internal stages, and the discipline is in which stage is allowed to do what.
The consequence of building sight as a faculty rather than a pipeline is that vision inherits the substrate’s whole constitution for free. It is grounded (a recognition traces to a signature plus a learned rule or an instance match), revisable (a belief, not a verdict), legible (one can ask why and get a chain, not an activation), and singular (one authority for what has been seen). And because the naming lives in the substrate, a thing seen and a thing read and a thing told are the same kind of knowledge: the substrate can be told what a new object is in words, and recognise it by sight afterwards, because both routes end at the same concept.
None of the second half of the faculty is speculative infrastructure. The substrate’s admission door already accepts perception as a first-class, root-level kind of evidence — a named thing observed in a named condition — distinct from a taught fact only in its provenance. It already types the values an observation carries, so a measured width or a capture date is held as a quantity or a time rather than as a word. Admitted observations already fan out to graded belief and to domain formation through the one learning path. And the induction that would learn a signature-to-concept rule is the same induction the substrate already uses to learn action rules and to learn readings from example sentences. The faculty’s first stage — the classical perception — is the genuinely new code, and it is a composition of algorithms that have existed for decades and ship in mature libraries. The second stage plugs into a door that is already open; the third stage is a faculty the substrate already runs. Integration is a matter of building the eye and wiring it to organs that already exist, not of inventing the organs.
The claim of this paper is deliberately narrow and, we think, therefore strong. We do not claim that pixels can be named by algorithm alone — the literature is clear that they cannot, and that every system which appears to do so has a trained model inside it. We claim that naming is the only part that must be learned, that the rest of seeing is algorithmic, and that a substrate which already learns concepts is the right thing to do the naming — not a network trained on someone else’s labels and frozen, but a learner that improves with what it is shown and reasons with what it recognises.
Built this way, sight is not a bolted-on classifier but an organ continuous with the rest of the mind. A recognised object is a concept, so it inherits, participates in inference, and can be reasoned about the instant it is seen. It is a belief, so it can be doubted, corroborated, and revised. It has a provenance, so the substrate can always say it knows this because it saw it, in this frame. And it is produced at one entry point, so there is a single, auditable answer to what the system has perceived. The separation of structure from meaning — perceiving first, naming second, by different faculties — is not merely a tidy decomposition. It is what lets a seeing machine tell you why it thinks it saw what it saw, and lets it say, without embarrassment, when it has seen a shape it cannot yet name. Structure before meaning is how sight becomes legible.